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Dynamical models used in climate prediction often have systematic errors that can deteriorate predictions. In this study, we work in a twin experiment framework with a reduced-order coupled ocean-atmosphere model and aim to demonstrate the benefit of machine learning for climate prediction. Machine learning is applied to learn the model error and thus build a data-driven model to emulate the dynamical model error. Then we build a hybrid model by combining the data-driven and dynamical models. The prediction skill of the hybrid model is compared to that of the standalone dynamical model. We applied this approach to the ocean-atmosphere coupled model. The results show that the hybrid model outperforms the dynamical model alone for both atmospheric and oceanic variables. Also, we build two other hybrid models only correcting either atmospheric errors or oceanic errors. It was found that correcting both atmospheric and oceanic errors leads to the best performance.

Accurately inverting global and regional subsurface temperature (ST) by multisource satellite observations is a challenging but hot topic. This study proposes a new method to invert daily ST from the sea surface information in China's marginal seas based on generative adversarial network (GAN) model. The proposed GAN‐based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12°. A traditional regression‐based model, Modular Ocean Data Assimilation System (MODAS), is set up same experiments for comparison. The results show that the averaged root mean square error results are less than 1.45°C in the upper 200 m and the highest averaged R2 of 0.97 at the 70 m level, which is better than that of MODAS. Errors analysis and typical oceanographic phenomena analysis results show the superiority of the proposed GAN‐based model in this study. This study can provide high‐precision daily ST data from sea surface information, which can be expanded to further studies on the interior ocean variation characteristics.

Using the Alfred Wegener Institute Climate Model (AWI-CM 1.1 LR), we conduct sensitivity experiments separating the Arctic and extra-Arctic warming to investigate the transient response of AMOC to quadrupled carbon dioxide (4×CO2) forcings. The results suggest that AMOC weakening is primarily affected by circulation adjustment induced by the outer-Arctic warming, while the effects of Arctic warming are confined to the polar range and contribute less to AMOC changes. When warming forcing is applied outside the Arctic, the increases of northward advective heat transport dominate the weakening of deep convection in the Nordic Seas, while the reduction of heat loss from ocean to atmosphere is prevalent in Labrador Sea. Besides, the weakening of deep convection in Nordic Seas is more pronounced than in Labrador Sea, implying a leading role of Nordic Seas in the weakening of AMOC under global warming.